A sequential Monte Carlo algorithm for inference of subclonal structure in cancer

Abstract

Tumors are heterogeneous in the sense that they consist of multiple subpopulations of cells, referred to as subclones, each of which is characterized by a distinct profile of genomic variations such as somatic mutations. Inferring the underlying clonal landscape has become an important topic in that it can help in understanding cancer development and progression, and thereby help in improving treatment. We describe a novel state-space model, based on the feature allocation framework and an efficient sequential Monte Carlo (SMC) algorithm, using the somatic mutation data obtained from tumor samples to estimate the number of subclones, as well as their characterization. Our approach, by design, is capable of handling any number of mutations. Via extensive simulations, our method exhibits high accuracy, in most cases, and compares favorably with existing methods. Moreover, we demonstrated the validity of our method through analyzing real tumor samples from patients from multiple cancer types (breast, prostate, and lung). Our results reveal driver mutation events specific to cancer types, and indicate clonal expansion by manual phylogenetic analysis. MATLAB code and datasets are available to download at: https://github.com/moyanre/tumor_clones.